C. Maguire, P. de Chazal, R. B. Reilly, P. Lacy “Automatic Classification of voice pathology using speech analysis”, World Congress on Biomedical Engineering and Medical Physics, Sydney, August 2003; and C. Maguire, P. de Chazal, R. B. Reilly, P. Lacy “Identification of Voice Pathology using Automated Speech Analysis”, Proc. of the 3rd International Workshop on Models and Analysis of Vocal Emission for Biomedical Applications, Florence, December 2003 disclose methods to aid in early detection, diagnosis, assessment and treatment of laryngeal disorders including feature extraction from acoustic signals to aid diagnosis.
J. I. Godino-Llorente, P Gomez-Vilda, “Automatic Detection of Voice Impairments by means of Short-Term Cepstral Parameters and Neural Network Based Detectors” IEEE Transactions on Biomedical Engineering Vol. 51, No. 2, pp. 380-384, February 2004 discloses a neural network based detector that is based on short-term cepstral parameters for discrimination between normal and abnormal speech samples. Using a subset of 135 voices from a publicly available database, Mel frequency cepstral coefficients (MFCCs) and their derivatives were employed as input features to a classifier which achieved an accuracy of 96.0% in classifying normal and abnormal voices.
Common to these and other prior art pathology detection systems is the recording environments of the voice samples under test. These comprise controlled recordings (soundproof recording room, set distance from patient to microphone) recorded at a sampling rate of approximately 25 kHz.